1. Field of the Invention
The present invention relates to a method and an apparatus for retrieving similar objects using the object's feature quantity and particularly to a method and an apparatus for retrieving similar objects using the multimedia object's feature quantity.
2. Description of the Related Art
In recent years, multimedia object data has been used in various situations. The multimedia object data includes still pictures, motion pictures, voice, and music. Concerning data for expressing a 3-D object, for example, a solid shape, there are extensively used 3-D object data for merchandise, a digital archive of 3-D object data for archaeology, art objects, etc., in addition to conventionally used CAD data. Further, a large amount of digital image data and digital music data is interchanged via the Internet. These types of data are increasing. There is an increasing demand for effectively managing data and retrieving data requested by users.
Various technologies are proposed to satisfy these demands. With respect to the technology for retrieving similar objects, there is proposed a method of calculating features of multimedia objects such as the above-mentioned images, music, etc., and performing retrieval based on feature quantities. According to the similar object retrieval method based on feature quantities, a user first specifies an object intended to be a retrieval result. The feature quantity of the specified object is compared to that of an object registered in the database. Doing so makes it possible to retrieve a similar object which resembles the object intended by the user. In this case, it is a general practice to set a weighting factor to represent importance of feature quantities (color, shape, etc. for representing the feature). Appropriately setting the weighting factor makes it possible to retrieve objects that differ in color but have similar shape. There are proposed methods of properly setting the weighting factor for effective retrieval. An example is Jpn. Pat. Appln. KOKAI Publication No. 2000-285141 titled “Image retrieval device, image classifying device, and computer readable recording medium recorded with program for functioning computer as these devices”. In this publication, there is proposed an image retrieval apparatus capable of specifying the importance of various types of feature quantities attributed to an image. Using this apparatus, a user can specify the importance of various types of feature quantities attributed to an image and retrieve images based on the feature assumed to be more important.
In the Jpn. Pat. Appln. KOKAI Publication No. 9-101970 “Method and device for retrieving image”, there is proposed an image retrieval method having the steps of extracting feature quantities having approximate values between a plurality of candidate images and increasing the weighting of these feature quantities. This method enables weighting of the feature quantity based on values between a plurality of candidate images selected from the database. This proposal enables images obtained as a result of the similarity retrieval to be re-selected as candidate images.
However, conventional methods make it very difficult to obtain an intended retrieval result unless a user fully understands the meaning of the feature quantity for specifying the weighting factor. According to Jpn. Pat. Appln. KOKAI Publication No. 2000-285141, for example, it is possible to specify the importance for each type of feature quantity attributed to an image. However, to obtain an intended retrieval result, the user must correctly understand the type of feature quantities corresponding to that result and specify the importance for each feature quantity. All feature quantities of objects are not always intuitive. In many cases, there is no one-to-one correspondence between an intended retrieval result and a feature quantity item. A plurality of feature quantity items is required for obtaining an intended retrieval result. Accordingly, it is very difficult and inefficient to manually specify the importance for each feature quantity for obtaining an intended retrieval result.
According to Jpn. Pat. Appln. KOKAI Publication No. 9-101970, it is possible to extract feature quantities having approximate values between a plurality of candidate images and automatically change the weighting of these feature quantities. In reality, however, feature quantities in the database must be normalized for all objects included in the retrieval range in order to determine the importance of the feature just by using the approximation of feature quantity values. A general retrieval system adjusts a retrieval range according to the object as a retrieval key and a user intention. Let us assume to retrieve a chair similar to an intended one from an interior database. In this case, the general retrieval system limits the retrieval range to a minimum including chairs, not to the entire database. Changing the retrieval range varies the statistic of objects included in the retrieval range, making it difficult to normalize feature quantities beforehand. Namely, the method of automatically weighting feature quantities in Jpn. Pat. Appln. KOKAI Publication No. 9-101970 is impractical because the method requires the database to be normalized in the current retrieval range. When an attempt is made to retrieve an object not present in the database, feature quantities including that object are not normalized. It is difficult to automatically weight feature quantities according to the technique in Jpn. Pat. Appln. KOKAI Publication No. 9-101970. Each time the retrieval range is changed, feature quantities need to be normalized in accordance with the changed retrieval range. Otherwise, it is difficult to compare feature quantities for a plurality of selected candidate images and to determine whether or not the compared values are approximate to each other. Consequently, it is impossible to provide an effect of automating the weighting of feature quantities.